While regression analysis is widely understood, it falls short in determining the causal direction of relationships in observational data. In this groundbreaking volume, Wiedermann and von Eye introduce Direction Dependence Analysis (DDA), a novel method that leverages variable information often overlooked by traditional techniques, such as higher-order moments like skewness and kurtosis. DDA reveals the asymmetry properties of regression and correlation, enabling researchers to evaluate competing causal hypotheses, assess the roles of variables in causal flows, and develop statistical methods for testing causal direction. This book provides a comprehensive formal description of DDA, illustrated with both artificial and real-world data examples. Additionally, readers will find free software implementations of DDA, making this an essential resource for researchers seeking to enhance their understanding of causal relationships in data analysis.
1. Introduction; 2. The linear regression model; 3. Asymmetry properties of distributions of observed variables; 4. Asymmetry properties of error distributions; 5. Independence properties of causes and errors; 6. Direction of dependence under latent confounding; 7. The integrated framework of Direction Dependence Analysis; 8. Stability and sensitivity analyses; 9. Extensions and applications; 10. Statistical software; 11. Concluding remarks.
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